Technical Skills

5+ years in data engineering, AI engineering, or knowledge graph development

· Hands-on experience with knowledge graph technologies (property graph and/or RDF frameworks)

· Experience with Cypher and/or SPARQL

· Strong data modeling and ontology design skills

· Experience integrating relational databases and external data sources

· Strong Python development skills

· Experience integrating LLMs into enterprise applications

· Familiarity with RAG architecture and AI-driven recommendation systems

Roles & Responsibilities

· Design and implement knowledge graph architecture using property graph and/or RDF-based models

· Transform and integrate structured and semi-structured data into optimized graph structures

· Develop and query graph systems using Cypher and/or SPARQL

· Design ontologies and entity relationship models to support sales intelligence use cases

· Integrate knowledge graphs with LLMs using Retrieval-Augmented Generation (RAG) architecture

· Build APIs and application services to deliver AI-driven prospecting insights

· Implement scoring, relationship strength analysis, and network traversal logic

· Ensure scalability, security, and enterprise-grade performance

· Partner with sales and business stakeholders to translate requirements into technical solutions

Preferred Qualifications

· Experience building sales intelligence or CRM-adjacent platforms

· Knowledge of embeddings, semantic search, and vector databases

· Experience designing relationship scoring or network analytics models · Cloud platform experience (AWS, Azure, or GCP)

· Experience in financial services or insurance environments.

Success Criteria

· Deliver a scalable knowledge graph + AI solution that enhances sales prospect identification and relationship insights

· Enable measurable improvements in targeting precision and cross-sell opportunity identification

· Establish a reusable architecture pattern for enterprise AI-driven sales initiatives

ClifyX

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